Abstract
The aim of this paper is to analyze the ability of cooperative coevolution to improve the scalability of population based metaheuristics. An extensive set of experiments on high dimensional optimization problems has been conducted in order to study the particularities and effectiveness of some elements involved in the design of cooperative coevolutionary algorithms: groupings of variables into components, choice of the context based on which the components are evaluated, length of evolution for each component. Scalability improvements have been obtained in the case of both analyzed metaheuristics: differential evolution and harmony search.
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Crăciun, C., Nicoară, M., Zaharie, D. (2010). Enhancing the Scalability of Metaheuristics by Cooperative Coevolution. In: Lirkov, I., Margenov, S., Waśniewski, J. (eds) Large-Scale Scientific Computing. LSSC 2009. Lecture Notes in Computer Science, vol 5910. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12535-5_36
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DOI: https://doi.org/10.1007/978-3-642-12535-5_36
Publisher Name: Springer, Berlin, Heidelberg
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